Lab9 leafmap

Question 1: Creating an Interactive Map¶

In [1]:

import leafmap

import leafmap

In [2]:

m = leafmap.Map()
url = "https://nominatim.openstreetmap.org/search?format=json&q={s}"
m.add_search_control(url, placeholder="Search for a place", position="topright", zoom=6)
m

m = leafmap.Map() url = "https://nominatim.openstreetmap.org/search?format=json&q={s}" m.add_search_control(url, placeholder="Search for a place", position="topright", zoom=6) m

Out[2]:

Question 2: Adding Map Legends¶

In [3]:

map = leafmap.Map()
url = "https://services.terrascope.be/wms/v2?"
map.add_wms_layer(
    url,
    name="World Landcover",
    opacity=0.8,
    layers="WORLDCOVER_2021_MAP",
    transparent=True,
)
map.add_legend(
    builtin_legend="ESA_WorldCover",
)
map

map = leafmap.Map() url = "https://services.terrascope.be/wms/v2?" map.add_wms_layer( url, name="World Landcover", opacity=0.8, layers="WORLDCOVER_2021_MAP", transparent=True, ) map.add_legend( builtin_legend="ESA_WorldCover", ) map

Out[3]:

Question 3: Creating Marker Clusters¶

Create a marker cluster visualization from a GeoJSON file of building centroids¶

In [4]:

map_1 = leafmap.Map(
    basemap="Google Satellite", center=(47.653441, -117.597316), zoom=16
)
map_1

map_1 = leafmap.Map( basemap="Google Satellite", center=(47.653441, -117.597316), zoom=16 ) map_1

Out[4]:

In [5]:

import geopandas as gpd

import geopandas as gpd

In [6]:

gdf = gpd.read_file(
    "https://github.com/opengeos/datasets/releases/download/places/wa_building_centroids.geojson"
)
gdf.head()

gdf = gpd.read_file( "https://github.com/opengeos/datasets/releases/download/places/wa_building_centroids.geojson" ) gdf.head()

Out[6]:

	Class	Confidence	Shape_Leng	Shape_Area	geometry
0	1	28.905460	68.457069	292.568026	POINT (-117.60109 47.65499)
1	1	99.420196	97.152785	556.702899	POINT (-117.59953 47.65533)
2	1	95.450807	90.916993	492.940128	POINT (-117.59991 47.65514)
3	1	91.446453	85.645123	453.842109	POINT (-117.59953 47.65575)
4	1	90.172392	78.057638	380.403649	POINT (-117.59989 47.65534)

In [7]:

# Extract longitude and latitude from geometry and append to gdf
gdf["longitude"] = gdf.geometry.x
gdf["latitude"] = gdf.geometry.y
gdf.head()

# Extract longitude and latitude from geometry and append to gdf gdf["longitude"] = gdf.geometry.x gdf["latitude"] = gdf.geometry.y gdf.head()

Out[7]:

	Class	Confidence	Shape_Leng	Shape_Area	geometry	longitude	latitude
0	1	28.905460	68.457069	292.568026	POINT (-117.60109 47.65499)	-117.601092	47.654993
1	1	99.420196	97.152785	556.702899	POINT (-117.59953 47.65533)	-117.599525	47.655326
2	1	95.450807	90.916993	492.940128	POINT (-117.59991 47.65514)	-117.599910	47.655143
3	1	91.446453	85.645123	453.842109	POINT (-117.59953 47.65575)	-117.599532	47.655747
4	1	90.172392	78.057638	380.403649	POINT (-117.59989 47.65534)	-117.599892	47.655336

In [8]:

map_1.add_marker_cluster(
    gdf,
    x="longitude",
    y="latitude",
    layer_name="Building Centroids",
    radius=5,
    outline_color="red",
    fill_color="yellow",
    fill_opacity=0.8,
)

map_1.add_marker_cluster( gdf, x="longitude", y="latitude", layer_name="Building Centroids", radius=5, outline_color="red", fill_color="yellow", fill_opacity=0.8, )

Create circle markers for each building centroid using the Map.add_circle_markers_from_xy() method¶

In [9]:

map_2 = leafmap.Map(
    basemap="Google Satellite", center=(47.653441, -117.597316), zoom=16
)

map_2 = leafmap.Map( basemap="Google Satellite", center=(47.653441, -117.597316), zoom=16 )

In [10]:

map_2.add_circle_markers_from_xy(
    gdf,
    x="longitude",
    y="latitude",
    radius=5,
    color="red",
    fill_color="yellow",
    fill_opacity=0.8,
    layer_name="Building Centroids",
    zoom_to_layer=True,
)
map_2

map_2.add_circle_markers_from_xy( gdf, x="longitude", y="latitude", radius=5, color="red", fill_color="yellow", fill_opacity=0.8, layer_name="Building Centroids", zoom_to_layer=True, ) map_2

Out[10]:

Question 4: Visualizing Vector Data¶

Visualize the building polygons GeoJSON file and style it with:¶

In [11]:

map_3 = leafmap.Map(basemap="Google Satellite")
data = "https://github.com/opengeos/datasets/releases/download/places/wa_overture_buildings.geojson"
style = {"color": "red", "weight": 1.5, "fillOpacity": 0}
map_3.add_vector(data, layer_name="Overture Buildings", style=style, zoom_to_layer=True)
map_3

map_3 = leafmap.Map(basemap="Google Satellite") data = "https://github.com/opengeos/datasets/releases/download/places/wa_overture_buildings.geojson" style = {"color": "red", "weight": 1.5, "fillOpacity": 0} map_3.add_vector(data, layer_name="Overture Buildings", style=style, zoom_to_layer=True) map_3

Out[11]:

Visualize the road polylines GeoJSON file and style it with:¶

In [12]:

map_4 = leafmap.Map(basemap="Google Satellite")
data = "https://github.com/opengeos/datasets/releases/download/places/las_vegas_roads.geojson"
style = {"color": "red", "weight": 2, "fillOpacity": 0}
map_4.add_vector(data, layer_name="Las Vegas Roads", style=style, zoom_to_layer=True)
map_4

map_4 = leafmap.Map(basemap="Google Satellite") data = "https://github.com/opengeos/datasets/releases/download/places/las_vegas_roads.geojson" style = {"color": "red", "weight": 2, "fillOpacity": 0} map_4.add_vector(data, layer_name="Las Vegas Roads", style=style, zoom_to_layer=True) map_4

Out[12]:

Create a choropleth map of county areas in the US:¶

In [13]:

url = "https://github.com/opengeos/datasets/releases/download/us/us_counties.geojson"
gdf = gpd.read_file(url)

map_5 = leafmap.Map(center=(37.5, -96), zoom=4)
map_5.add_data(
    gdf,
    column="CENSUSAREA",
    scheme="Quantiles",
    cmap="Blues",
    legend_title="Census Area",
)
map_5

url = "https://github.com/opengeos/datasets/releases/download/us/us_counties.geojson" gdf = gpd.read_file(url) map_5 = leafmap.Map(center=(37.5, -96), zoom=4) map_5.add_data( gdf, column="CENSUSAREA", scheme="Quantiles", cmap="Blues", legend_title="Census Area", ) map_5

Out[13]:

In [ ]:

Exercise 5: Creating a Split Map¶

In [14]:

Pre_event_imagery = (
    "https://github.com/opengeos/datasets/releases/download/raster/Libya-2023-07-01.tif"
)
Post_event_imagery = (
    "https://github.com/opengeos/datasets/releases/download/raster/Libya-2023-09-13.tif"
)

Pre_event_imagery = ( "https://github.com/opengeos/datasets/releases/download/raster/Libya-2023-07-01.tif" ) Post_event_imagery = ( "https://github.com/opengeos/datasets/releases/download/raster/Libya-2023-09-13.tif" )

In [15]:

map_6 = leafmap.Map()
map_6.split_map(
    left_layer=Pre_event_imagery,
    right_layer=Post_event_imagery,
    left_label="Pre-event Imagery",
    right_label="Post-event Imagery",
)
map_6

map_6 = leafmap.Map() map_6.split_map( left_layer=Pre_event_imagery, right_layer=Post_event_imagery, left_label="Pre-event Imagery", right_label="Post-event Imagery", ) map_6

Out[15]: